{"id":70898,"date":"2018-03-09T23:15:25","date_gmt":"2018-03-09T23:15:25","guid":{"rendered":"https:\/\/www.deberes.net\/tesis\/sin-categoria\/prediccion-a-corto-plazo-de-los-precios-de-la-energia-electrica\/"},"modified":"2018-03-09T23:15:25","modified_gmt":"2018-03-09T23:15:25","slug":"prediccion-a-corto-plazo-de-los-precios-de-la-energia-electrica","status":"publish","type":"post","link":"https:\/\/www.deberes.net\/tesis\/matematicas\/prediccion-a-corto-plazo-de-los-precios-de-la-energia-electrica\/","title":{"rendered":"Prediccion a corto plazo de los precios de la energia electrica"},"content":{"rendered":"<h2>Tesis doctoral de <strong> Rosario Espinola Vilchez <\/strong><\/h2>\n<p>El objetivo b\u00e1sico de esta tesis es el desarrollo de modelos que permitan la predicci\u00f3n de los precios horarios de la electricidad de un mercado de energ\u00eda el\u00e9ctrica con un d\u00eda de antelaci\u00f3n.  los modelos utilizados se basan en series temporales y la aplicaci\u00f3n de redes neuronales. Dentro de los modelos basados en series temporales se distinguen dos modelos: modelo de regresi\u00f3n din\u00e1mica y modelo arima.  los modelos de series temporales se aplican para obtener las predicciones de los precios para cada una de las semanas seleccionadas del a\u00f1o 2000 en el mercado peninsular espa\u00f1ol y tambi\u00e9n en la interconexi\u00f3n pjm para el a\u00f1o 2002. Los modelos de redes neuronales s\u00f3lo se aplican en la interconexi\u00f3n pjm. Se estudian estos dos mercados para comparar la eficacia de cada uno estos modelos en dos mercados diferentes.  con el objetivo de comparar las predicciones obtenidas con los diferentes modelos se define un modelo naive.  en este modelo naive la predicci\u00f3n para cada una de las 24 horas de un d\u00eda corresponde al precio real del d\u00eda anterior o la semana anterior, seg\u00fan corresponda. Los valores para la predicci\u00f3n de un lunes corresponden a los precios reales del lunes anterior. An\u00e1logamente sucede con los s\u00e1bados y domingos; los valores predichos son los precios reales de s\u00e1bados y domingos anteriores. En cambio, para martes, mi\u00e9rcoles, jueves y viernes, la predicci\u00f3n para cada uno de estos d\u00edas es la correspondiente al precio real del d\u00eda anterior al que se quiere predecir.  con este m\u00e9todo naive se obtienen buenas predicciones en zonas donde el precio es estable y tambi\u00e9n cuando existe un comportamiento similar de una semana a la siguiente y de un d\u00eda al siguiente.  las tareas llevadas a cabo en esta tesis son las siguientes:  1. Desarrollo de varios modelos de series temporales para la predicci\u00f3n de los precios de la electricidad. Utilizaci\u00f3n de varios modelos de series temporales, como son el modelo ari<\/p>\n<p>&nbsp;<\/p>\n<h3>Datos acad\u00e9micos de la tesis doctoral \u00ab<strong>Prediccion a corto plazo de los precios de la energia electrica<\/strong>\u00ab<\/h3>\n<ul>\n<li><strong>T\u00edtulo de la tesis:<\/strong>\u00a0 Prediccion a corto plazo de los precios de la energia electrica <\/li>\n<li><strong>Autor:<\/strong>\u00a0 Rosario Espinola Vilchez <\/li>\n<li><strong>Universidad:<\/strong>\u00a0 Castilla-la mancha<\/li>\n<li><strong>Fecha de lectura de la tesis:<\/strong>\u00a0 22\/10\/2004<\/li>\n<\/ul>\n<p>&nbsp;<\/p>\n<h3>Direcci\u00f3n y tribunal<\/h3>\n<ul>\n<li><strong>Director de la tesis<\/strong>\n<ul>\n<li>Antonio  Jes\u00fas Conejo Navarro<\/li>\n<\/ul>\n<\/li>\n<li><strong>Tribunal<\/strong>\n<ul>\n<li>Presidente del tribunal: Carlos Alvarez bel <\/li>\n<li> Nogales martin Francisco Javier (vocal)<\/li>\n<li> Pedregal tercero diego Jos\u00e9 (vocal)<\/li>\n<li>Jes\u00fas Riquelme santos (vocal)<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<p>&nbsp;<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Tesis doctoral de Rosario Espinola Vilchez El objetivo b\u00e1sico de esta tesis es el desarrollo de modelos que permitan la [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"site-sidebar-layout":"default","site-content-layout":"","ast-site-content-layout":"","site-content-style":"default","site-sidebar-style":"default","ast-global-header-display":"","ast-banner-title-visibility":"","ast-main-header-display":"","ast-hfb-above-header-display":"","ast-hfb-below-header-display":"","ast-hfb-mobile-header-display":"","site-post-title":"","ast-breadcrumbs-content":"","ast-featured-img":"","footer-sml-layout":"","theme-transparent-header-meta":"","adv-header-id-meta":"","stick-header-meta":"","header-above-stick-meta":"","header-main-stick-meta":"","header-below-stick-meta":"","astra-migrate-meta-layouts":"default","ast-page-background-enabled":"default","ast-page-background-meta":{"desktop":{"background-color":"var(--ast-global-color-4)","background-image":"","background-repeat":"repeat","background-position":"center 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